Collate the six figures from the programming lab in one PDF document. For each figure, write a caption that clearly describes what the figure displays.
Output: Upload the following file to codePost: ps10.pdf
Use the code from the programming lab to create one function that puts all the steps for training and testing a neural network together. To do so, complete the missing pieces in the code below.
# import libraries
import numpy as np
from numpy import random
import numpy.matlib
import matplotlib.pyplot as plt
# main function
def ps10(outi, niterations, alpha, monitor):
# seed the random number generator (to ensure that we all generate the same data and initial weights)
random.seed(3)
# generate training data
means = (0.3,0.7)
sigma = 0.16
ndatapoints_train = 128
data_output_train = # complete this yourself
data_train = data_output_train[0]
randvec_train = data_output_train[1]
# generate test data
means = (0.3,0.7)
sigma = 0.16
ndatapoints_test = 128
data_output_test = # complete this yourself
data_test = data_output_test[0]
randvec_test = data_output_test[1]
# unpack training and testing data
X_train = # complete this yourself
y_train = # complete this yourself
X_test = # complete this yourself
y_test = # complete this yourself
# initialize weights
nweights = 2
randn = 0
w, b = # complete this yourself
# optimize
weights, gradients, costs = # complete this yourself
# predict
p_train = # complete this yourself
p_test = # complete this yourself
# show training and test data and decision boundaries
if monitor:
plt.ion()
# training data
colors = plot_data(ndatapoints_train, data_output_train, 1)
plot_boundary(weights, 1)
# test data
colors = plot_data(ndatapoints_test, data_output_test, 2)
plot_boundary(weights, 2)
# print results
if outi == 1:
print("cost at start and end:",costs[0],costs[niterations-1])
if outi == 2:
print("train accuracy: {} %".format(100 - np.mean(np.abs(p_train - y_train)) * 100))
if outi == 3:
print("test accuracy: {} %".format(100 - np.mean(np.abs(p_test - y_test)) * 100))
# helper functions
# generate data (input x and output f(x))
def generate_data(means, sigma, ndatapoints):
nclasses = 2
data = np.zeros((nclasses * ndatapoints, 3))
for c in range(0, nclasses):
starti = c * ndatapoints
endi = (c + 1) * ndatapoints
data[starti:endi, 0:2] = means[c] + sigma * random.standard_normal((ndatapoints, 2))
data[starti:endi, 2] = c
randvec = np.random.permutation(nclasses * ndatapoints)
data = data[randvec,:]
return data, randvec;
# plot the data
def plot_data(ndatapoints, data_output, figi):
data = data_output[0]
randvec = data_output[1]
colors = np.concatenate((np.matlib.repmat(np.array([1, 0.5, 1]),ndatapoints,1),np.matlib.repmat(np.array([0.5, 1, 1]),ndatapoints,1),np.matlib.repmat(np.array([0.6, 1, 0.6]),ndatapoints,1),np.matlib.repmat(np.array([0.5, 0.5, 1]),ndatapoints,1)))
colors = colors[randvec,:]
plt.figure(figi)
plt.scatter(data[:,0], data[:,1], c=colors, alpha=0.5)
plt.axis('square')
plt.xlabel('x1 (0 = green, 1 = red)')
plt.ylabel('x2 (0 = small, 1 = large)')
if figi == 1:
plt.title('training data')
elif figi == 2:
plt.title('test data')
return colors
# initialize weights
def initialize_weights(nweights, randn):
if randn == 0:
w = np.zeros( (nweights, 1) )
else:
w = 0.001 * random.standard_normal( (nweights, 1) )
b = 0
return w, b
# sigmoid activation function
def sigmoid(z):
# complete this yourself (1 line)
return a
# propagate forward
def forward(X, w, b):
# complete this yourself (2 lines)
return a
# compute cost
def compute_cost(a, y):
# complete this yourself (2 lines)
return cost
# propagate back
def back(a, y, X):
# complete this yourself (3 lines)
return dw, db
# plot the decision boundary
def plot_boundary(weights, figi):
b = weights['b']
w = weights['w']
slope = -(b / w[1]) / (b / w[0])
y_intercept = -b / w[1]
x = np.linspace(0,1,100)
y = (slope * x) + y_intercept
plt.figure(figi)
plt.plot(x, y)
plt.pause(0.004)
# optimize using gradient descent
def optimize(w, b, X, y, niterations, alpha, monitor):
costs = []
for i in range(niterations):
a = forward(X, w, b)
cost = compute_cost(a, y)
dw, db = back(a, y, X)
w = # update the weights (complete this yourself)
b = # update the bias term (complete this yourself)
costs.append(cost)
if monitor and i % 100 == 0:
print ("Cost after iteration %i: %f" %(i, cost))
weights = {"w": w,
"b": b}
gradients = {"dw": dw,
"db": db}
return weights, gradients, costs
# predict
def predict(data, weights):
X = np.transpose( data[:,0:2] )
y = np.transpose(data[:,2:3])
a = forward(X, weights['w'], weights['b'])
p = np.zeros(a.shape)
for i in range(a.shape[1]):
if a[0,i] ‹= 0.5:
p[0,i] = 0
else:
p[0,i] = 1
return pTo test your code, save it as solution_code_ps10.py and run it using the code below.
# save the code below as ps10_main.py in the same folder as your solution code
#
# input arguments for ps10
# outi: output index
# controls the output that is printed to the terminal
# 1 = cost before and after training
# 2 = training accuracy
# 3 = test accuracy
# niterations: number of iterations through the training set (epochs)
# alpha: learning rate
# monitor: controls figure output
# 0 = no figures
# 1 = figures
#
# to test your solution code, set the input arguments below, and execute the following command from your terminal: run ps10_main.py
from solution_code_ps10 import ps10
ps10(
outi = 1, # if you want to print the cost before and after training
niterations = 2000,
alpha = 0.5,
monitor = 0,
)Output: Upload the following file to codePost: solution_code_ps10.py